Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods

The inwardly rectifying potassium channel Kir3.2, a member of the inward rectifier potassium (Kir) channel family, exerts important biological functions through transporting potassium ions outside of the cell, during which a large-scale synergistic movement occurs among its different domains. Currently, it is not fully understood how the binding of the ligand to the Kir3.2 channel leads to the structural changes and which key residues are responsible for the channel gating and allosteric dynamics. Here, we construct the Gaussian network model (GNM) of the Kir3.2 channel with the secondary structure and covalent interaction information considered (sscGNM), which shows a better performance in reproducing the channel's flexibility compared with the traditional GNM. In addition, the sscANM-based perturbation method is used to simulate the channel's conformational transition caused by the activator PIP2's binding. By applying certain forces to the PIP2 binding pocket, the coarse-grained calculations generate the similar conformational changes to the experimental observation, suggesting that the topology structure as well as PIP2 binding are crucial to the allosteric activation of the Kir3.2 channel. We also utilize the sscGNM-based thermodynamic cycle method developed by us to identify the key residues whose mutations significantly alter the channel's binding free energy with PIP2. We identify not only the residues important for the specific binding but also the ones critical for the allosteric transition coupled with PIP2 binding. This study is helpful for understanding the working mechanism of Kir3.2 channels and can provide important information for related drug design.

Bayesian network models identify co-operative GPCR:G protein interactions that contribute to G protein coupling

Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network-like behavior of interface interactions and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein-coupled receptor (GPCR) complexes with G proteins. Our results reveal a strong co-dependency in the formation of interface GPCR:G protein contacts. This observation indicates that cooperativity of GPCR:G protein interactions is necessary for the coupling and selectivity of G proteins and is thus critical for receptor function. We have identified subnetworks containing polar and hydrophobic interactions that are common among multiple GPCRs coupling to different G protein subtypes (Gs, Gi and Gq). These common subnetworks along with G protein-specific subnetworks together confer selectivity to the G protein coupling. This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.

Study of the Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase by Transfer Entropy via an Improved Gaussian Network Model and Co-evolution Analyses

We propose an improved transfer entropy approach called the dynamic version of the force constant fitted Gaussian network model based on molecular dynamics ensemble (dfcfGNM_MD) to explore the allosteric mechanism of human mitochondrial phenylalanyl-tRNA synthetase (hmPheRS), one of the aminoacyl-tRNA synthetases that play a crucial role in translation of the genetic code. The dfcfGNM_MD method can provide reliable estimates of the transfer entropy and give new insights into the role of the anticodon binding domain in driving the catalytic domain in aminoacylation activity and into the effects of tRNA binding and residue mutation on the enzyme activity, revealing the causal mechanism of the allosteric communication in hmPheRS. In addition, we incorporate the residue dynamic and co-evolutionary information to further investigate the key residues in hmPheRS allostery. This study sheds light on the mechanisms of hmPheRS allostery and can provide important information for related drug design.